{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# spaCyチュートリアル\n",
    "#### 01 spaCy初級"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## spaCyのインストール"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # spaCyのインストール\n",
    "# !conda install spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import ginza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 学習済みモデルをダウンロード"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "spaCyは英語、ドイツ語、フランス語、スペイン語、ポルトガル語、イタリア語、オランダ語、ギリシャ語等の多言語をサポートしています。  \n",
    "\n",
    "英語モデルでは基本的に小・中・大のサイズから選ぶことになり、精度と速度はトレードオフの関係にあります。また小サイズには単語ベクトルが付属していないなどの制限があります。  \n",
    "\n",
    "モデルファイルには重み、語彙、モデルパイプラインのメタ情報が含まれています。 \n",
    "\n",
    "日本語はMegagon Labsの[GiNZA](https://github.com/megagonlabs/ginza)が公開されているのでそれを使用します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # GiNZAのインストール\n",
    "# !pip install -U ginza\n",
    "# # モデルのダウンロード後にカーネルを再起動します"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## モデルを読み込む"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nlpオブジェクトを作成\n",
    "nlp = spacy.load(\"ja_ginza\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 例文\n",
    "例文 = \"\"\"どうも、藤本唯美です。XYZ社でデータサイエンティストとして一生懸命働いています。\\\n",
    "場所は東京都の新宿で、最高の街です! 😊 \\\n",
    "なんとラーメン一杯が７００円で食べれます（令和元年１２月１３日現在）。 (^_^)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nlpオブジェクトで文字列を処理する\n",
    "文章 = nlp(例文)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## トークン化\n",
    "\n",
    "トークン化とは文章をトークンと呼ばれるものに分割することです。英語ではすでに空白文字により分かち書きされているので、句読点や絵文字などを除き基本的には空白文字を境に単語単位でトークン化することが多いです。ほとんどのspaCyの機能はカスタマイズ可能で、例えば英語のトークン化の際に名詞の間のinfixであるハイフンを分かち書きルールに加えたりできます。  \n",
    "\n",
    "![token](img/token.png)  \n",
    "\n",
    "日本語や中国語、タイ語などは空白文字で分かち書きされていません。 その場合は  \n",
    "1. SudachiPy、MeCabやJanomeなどの形態素解析のライブラリを使って分かち書きする。  \n",
    "2. バイト対符号化（Byte Pair Encoding -BPE）でサブワードに分割する.  \n",
    "BPEはGoogleのBERTで使用されているように、英語でも効果的だとされています。  \n",
    "\n",
    "GiNZAはトークン化（形態素解析）処理にSudachiPyを使用しています。\n",
    "\n",
    "**階層:** コーパス > 文章（ドキュメント） > 文 > 単語 > サブワード > 文字 > 部首 > 書き順  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 どう\n",
      "2 も\n",
      "3 、\n",
      "4 藤本\n",
      "5 唯美\n",
      "6 です\n",
      "7 。\n",
      "8 XYZ\n",
      "9 社\n",
      "10 で\n",
      "11 データ\n",
      "12 サイエンティスト\n",
      "13 と\n",
      "14 し\n",
      "15 て\n",
      "16 一生懸命\n",
      "17 働い\n",
      "18 て\n",
      "19 い\n",
      "20 ます\n",
      "21 。\n",
      "22 場所\n",
      "23 は\n",
      "24 東京都\n",
      "25 の\n",
      "26 新宿\n",
      "27 で\n",
      "28 、\n",
      "29 最高\n",
      "30 の\n",
      "31 街\n",
      "32 です\n",
      "33 !\n",
      "34 😊\n",
      "35 なん\n",
      "36 と\n",
      "37 ラーメン\n",
      "38 一杯\n",
      "39 が\n",
      "40 ７００\n",
      "41 円\n",
      "42 で\n",
      "43 食べれ\n",
      "44 ます\n",
      "45 （\n",
      "46 令和\n",
      "47 元年\n",
      "48 １２\n",
      "49 月\n",
      "50 １３\n",
      "51 日\n",
      "52 現在\n",
      "53 ）\n",
      "54 。\n",
      "55 (^_^)\n"
     ]
    }
   ],
   "source": [
    "# トークンごとに番号とテキストをプリントする\n",
    "i = 0\n",
    "for トークン in 文章:\n",
    "    i += 1\n",
    "    print (f\"{i} {トークン.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ストップワード  \n",
    "ストップワードは高頻度語であり、下流タスク（単語頻度分析、トピックモデル、カウントベクトライザー、バッグオブワーズ（特徴量数減少に役立つ）など）には必要の無いとされるものです。  \n",
    "\n",
    "ストップワードの除去は深層学習にはむしろ悪影響であると主張する[AI研究者](https://twitter.com/deliprao/status/1068555626299584512)もいます。理由は打消しなどを意味する言葉を除去することで文の意味が変わってしまう可能性があるからです。  \n",
    "\n",
    "ストップワードリストはカスタマイズ可能です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GiNZAには150程のストップワードが含まれている \n",
    "ストップワード = ginza.STOP_WORDS\n",
    "len(ストップワード)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['つ',\n",
       " 'しまっ',\n",
       " 'そして',\n",
       " 'で',\n",
       " 'なっ',\n",
       " 'こう',\n",
       " 'はじめ',\n",
       " 'しよう',\n",
       " 'のち',\n",
       " 'おい',\n",
       " 'もと',\n",
       " 'よれ',\n",
       " 'さらに',\n",
       " 'よ',\n",
       " 'つい',\n",
       " 'いわ',\n",
       " 'の',\n",
       " 'その',\n",
       " 'そこ',\n",
       " 'よう',\n",
       " 'のみ',\n",
       " 'ご',\n",
       " 'なら',\n",
       " 'か',\n",
       " 'き',\n",
       " 'あるいは',\n",
       " 'おり',\n",
       " 'とっ',\n",
       " 'しか',\n",
       " '一']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30語のストップワードをプリントしてみる\n",
    "list(ストップワード)[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 見出し語化  \n",
    "見出し語化（Lemmatization）は単語を原型（lemma）に変換します。語幹処理（Stemming）と違いlemmaは実際の言葉として存在します。英語の例は下記の表をご覧ください。  \n",
    "\n",
    "見出し語化は品詞(**POS**)タグ付けに依存しており、（今のところは）ルールベースです。  \n",
    "\n",
    "見出し語化は正規化なので、ストップワードと同様に下流タスクで役に立つことがあります。しかし、深層学習ではこの前処理をしないことがほとんどです。  \n",
    "\n",
    "spaCyには語幹処理はありません。  \n",
    "\n",
    "GiNZAでもlemmaは辞書に出てくるような表記に正規化されるようです。\n",
    "\n",
    "単語 | Stem | Lemma\n",
    "--- | --- | ---\n",
    "Studies | Studi | Study\n",
    "Studying | Study | Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 どう \t\t どう\n",
      "2 も \t\t も\n",
      "3 、 \t\t 、\n",
      "4 藤本 \t\t 藤本\n",
      "5 唯美 \t\t 唯美\n",
      "6 です \t\t です\n",
      "7 。 \t\t 。\n",
      "8 XYZ \t\t xyz\n",
      "9 社 \t\t 社\n",
      "10 で \t\t で\n",
      "11 データ \t\t データ\n",
      "12 サイエンティスト \t\t サイエンティスト\n",
      "13 と \t\t と\n",
      "14 し \t\t 為る\n",
      "15 て \t\t て\n",
      "16 一生懸命 \t\t 一生懸命\n",
      "17 働い \t\t 働く\n",
      "18 て \t\t て\n",
      "19 い \t\t 居る\n",
      "20 ます \t\t ます\n",
      "21 。 \t\t 。\n",
      "22 場所 \t\t 場所\n",
      "23 は \t\t は\n",
      "24 東京都 \t\t 東京都\n",
      "25 の \t\t の\n",
      "26 新宿 \t\t 新宿\n",
      "27 で \t\t で\n",
      "28 、 \t\t 、\n",
      "29 最高 \t\t 最高\n",
      "30 の \t\t の\n",
      "31 街 \t\t 街\n",
      "32 です \t\t です\n",
      "33 ! \t\t ！\n",
      "34 😊 \t\t 😊\n",
      "35 なん \t\t 何\n",
      "36 と \t\t と\n",
      "37 ラーメン \t\t ラーメン\n",
      "38 一杯 \t\t 一杯\n",
      "39 が \t\t が\n",
      "40 ７００ \t\t 700\n",
      "41 円 \t\t 円\n",
      "42 で \t\t で\n",
      "43 食べれ \t\t 食べる\n",
      "44 ます \t\t ます\n",
      "45 （ \t\t （\n",
      "46 令和 \t\t 令和\n",
      "47 元年 \t\t 元年\n",
      "48 １２ \t\t 12\n",
      "49 月 \t\t 月\n",
      "50 １３ \t\t 13\n",
      "51 日 \t\t 日\n",
      "52 現在 \t\t 現在\n",
      "53 ） \t\t ）\n",
      "54 。 \t\t 。\n",
      "55 (^_^) \t\t （＾＿＾）\n"
     ]
    }
   ],
   "source": [
    "# トークンごとに番号とテキスト、見出し語をプリントする\n",
    "i = 0\n",
    "for トークン in 文章:\n",
    "    i += 1\n",
    "    print (f\"{i} {トークン.text} \\t\\t {トークン.lemma_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文単位分割  \n",
    "spaCyは文の境界を推論し、文章を文に分割することができます。他の機能同様カスタマイズ可能です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 どうも、藤本唯美です。\n",
      "2 XYZ社でデータサイエンティストとして一生懸命働いています。\n",
      "3 場所は東京都の新宿で、最高の街です!\n",
      "4 😊 なんとラーメン一杯が７００円で食べれます（令和元年１２月１３日現在）。 (^_^)\n"
     ]
    }
   ],
   "source": [
    "# 文ごとに番号とテキストをプリントする\n",
    "i = 0\n",
    "for 文 in 文章.sents:\n",
    "    i += 1\n",
    "    print (f\"{i} {文}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 統計モデル  \n",
    "spaCyには3種類の統計モデルが付属されています。  \n",
    "1. 品詞 (**POS**)タグ付け\n",
    "2. 固有表現抽出 (**NER**)\n",
    "3. 依存構文解析のラベル付け  \n",
    "\n",
    "spaCy英語モデルの学習方法はマルチタスクで教師ありseq2Seqの畳み込みニューラルネットワークで、サブワード埋め込みや 残差接続、層の正規化などを利用しています。自分のラベル付きデータを使い再学習されることも可能です。  \n",
    "\n",
    "GiNZAでは3は同様のアーキテキチャを採用しつつも[独自なやり方](https://www.anlp.jp/proceedings/annual_meeting/2019/pdf_dir/F2-3.pdf)をしているようです。具体的にはJapaneseCorrectorというパイプラインで形態素解析誤り（トークン分割、品詞）の補修をしています。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 品詞（**POS**）タグ付け  \n",
    "品詞は単語を文法的な機能や形態などによって分類したものです。  \n",
    "\n",
    "例:\n",
    "- Noun（名詞）\n",
    "- Pronoun（代名詞）\n",
    "- Proper noun（固有名詞）\n",
    "- Adjective（形容詞）\n",
    "- Verb（動詞）\n",
    "- Adverb（副詞）\n",
    "- Adposition（接置詞）\n",
    "- Auxiliary（助動詞）\n",
    "- Punctuation（句読）\n",
    "- Determiner（限定詞）\n",
    "- Subordinating conjunction（従属接続詞）\n",
    "- Interjection（間投詞）\n",
    "\n",
    "GiNZAにはUniDic短単位品詞も含まれています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 どう \t\t ADV \t\t 副詞 \t\t *,*\n",
      "2 も \t\t ADP \t\t 助詞-係助詞 \t\t *,*\n",
      "3 、 \t\t PUNCT \t\t 補助記号-読点 \t\t *,*\n",
      "4 藤本 \t\t PROPN \t\t 名詞-固有名詞-人名-姓 \t\t *,*\n",
      "5 唯美 \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "6 です \t\t AUX \t\t 助動詞 \t\t 助動詞-デス,終止形-一般\n",
      "7 。 \t\t PUNCT \t\t 補助記号-句点 \t\t *,*\n",
      "8 XYZ \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "9 社 \t\t NOUN \t\t 名詞-普通名詞-助数詞可能 \t\t *,*\n",
      "10 で \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "11 データ \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "12 サイエンティスト \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "13 と \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "14 し \t\t AUX \t\t 動詞-非自立可能 \t\t サ行変格,連用形-一般\n",
      "15 て \t\t SCONJ \t\t 助詞-接続助詞 \t\t *,*\n",
      "16 一生懸命 \t\t ADJ \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "17 働い \t\t VERB \t\t 動詞-一般 \t\t 五段-カ行,連用形-イ音便\n",
      "18 て \t\t SCONJ \t\t 助詞-接続助詞 \t\t *,*\n",
      "19 い \t\t AUX \t\t 動詞-非自立可能 \t\t 上一段-ア行,連用形-一般\n",
      "20 ます \t\t AUX \t\t 助動詞 \t\t 助動詞-マス,終止形-一般\n",
      "21 。 \t\t PUNCT \t\t 補助記号-句点 \t\t *,*\n",
      "22 場所 \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "23 は \t\t ADP \t\t 助詞-係助詞 \t\t *,*\n",
      "24 東京都 \t\t NOUN \t\t 名詞-固有名詞-地名-一般 \t\t *,*\n",
      "25 の \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "26 新宿 \t\t PROPN \t\t 名詞-固有名詞-地名-一般 \t\t *,*\n",
      "27 で \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "28 、 \t\t PUNCT \t\t 補助記号-読点 \t\t *,*\n",
      "29 最高 \t\t ADJ \t\t 名詞-普通名詞-形状詞可能 \t\t *,*\n",
      "30 の \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "31 街 \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "32 です \t\t AUX \t\t 助動詞 \t\t 助動詞-デス,終止形-一般\n",
      "33 ! \t\t PUNCT \t\t 補助記号-句点 \t\t *,*\n",
      "34 😊 \t\t PUNCT \t\t 補助記号-一般 \t\t *,*\n",
      "35 なん \t\t PRON \t\t 代名詞 \t\t *,*\n",
      "36 と \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "37 ラーメン \t\t NOUN \t\t 名詞-普通名詞-一般 \t\t *,*\n",
      "38 一杯 \t\t NOUN \t\t 名詞-普通名詞-形状詞可能 \t\t *,*\n",
      "39 が \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "40 ７００ \t\t NUM \t\t 名詞-数詞 \t\t *,*\n",
      "41 円 \t\t NOUN \t\t 名詞-普通名詞-助数詞可能 \t\t *,*\n",
      "42 で \t\t ADP \t\t 助詞-格助詞 \t\t *,*\n",
      "43 食べれ \t\t VERB \t\t 動詞-一般 \t\t 下一段-ラ行,連用形-一般\n",
      "44 ます \t\t AUX \t\t 助動詞 \t\t 助動詞-マス,終止形-一般\n",
      "45 （ \t\t PUNCT \t\t 補助記号-括弧開 \t\t *,*\n",
      "46 令和 \t\t PROPN \t\t 名詞-固有名詞-一般 \t\t *,*\n",
      "47 元年 \t\t NOUN \t\t 名詞-普通名詞-副詞可能 \t\t *,*\n",
      "48 １２ \t\t NUM \t\t 名詞-数詞 \t\t *,*\n",
      "49 月 \t\t NOUN \t\t 名詞-普通名詞-助数詞可能 \t\t *,*\n",
      "50 １３ \t\t NUM \t\t 名詞-数詞 \t\t *,*\n",
      "51 日 \t\t NOUN \t\t 接尾辞-名詞的-助数詞 \t\t *,*\n",
      "52 現在 \t\t NOUN \t\t 名詞-普通名詞-副詞可能 \t\t *,*\n",
      "53 ） \t\t PUNCT \t\t 補助記号-括弧閉 \t\t *,*\n",
      "54 。 \t\t PUNCT \t\t 補助記号-句点 \t\t *,*\n",
      "55 (^_^) \t\t SYM \t\t 補助記号-ＡＡ-顔文字 \t\t *,*\n"
     ]
    }
   ],
   "source": [
    "# トークンごとに番号とテキスト、品詞をプリントする \n",
    "i = 0\n",
    "for トークン in 文章:\n",
    "    i += 1\n",
    "    print (f\"{i} {トークン.text} \\t\\t {トークン.pos_} \\t\\t {トークン.tag_} \\t\\t {トークン._.inf}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 固有表現抽出（Named Entity Recognizer - **NER**）  \n",
    "固有表現抽出は固有表現の位置を推論し、あらかじめ定義されたカテゴリへと分類します。    \n",
    "- PERSON\t人名。キャラクター名含む。  \n",
    "- FAC\t建物、空港、高速道路、橋など。  \n",
    "- ORG\t組織名、会社、機関、施設など。  \n",
    "- GPE\t地名、国、町、県など。  \n",
    "- PRODUCT\t物、乗り物、食べ物など（サービス除く）。  \n",
    "- EVENT\t台風、戦い、戦争、スポーツのイベントなど。  \n",
    "- DATE\t日付や期間。  \n",
    "- TIME\t一日より小さい時間の単位。  \n",
    "- MONEY\t金額（通貨単位含む）。  \n",
    "- QUANTITY\t重量や距離。  \n",
    "- ORDINAL\t一番、二番など。  \n",
    "- CARDINAL\tその他の数量。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 藤本唯美 \t\t Person\n",
      "2 XYZ社 \t\t Company\n",
      "3 東京都 \t\t Province\n",
      "4 新宿 \t\t City\n",
      "5 ラーメン \t\t Dish\n",
      "6 ７００円 \t\t Money\n",
      "7 令和元年１２月１３日現在 \t\t Date\n"
     ]
    }
   ],
   "source": [
    "# 抽出されたトークンごとに番号とテキスト、固有表現をプリントする \n",
    "i = 0\n",
    "for トークン in 文章.ents:\n",
    "    i += 1\n",
    "    print (f\"{i} {トークン.text} \\t\\t {トークン.label_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 固有表現を視覚化する\n",
    "spacy.displacy.render(文章, style='ent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 依存構文解析のラベル付け  \n",
    "依存構文解析とは文章を形態素に切分け、その間の依存関係を抽出することです。抽出された構造は有向グラフとしてもみなされ、深層学習の様々な手法で使われています（木構造再帰ニューラルネット、グラフニューラルネットワークなど）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 どう \t\t ROOT \t\t どう\n",
      "2 も \t\t case \t\t どう\n",
      "3 、 \t\t punct \t\t どう\n",
      "4 藤本 \t\t compound \t\t 唯美\n",
      "5 唯美 \t\t punct \t\t どう\n",
      "6 です \t\t cop \t\t 唯美\n",
      "7 。 \t\t punct \t\t 唯美\n",
      "8 XYZ \t\t compound \t\t 社\n",
      "9 社 \t\t nmod \t\t 働い\n",
      "10 で \t\t case \t\t 社\n",
      "11 データ \t\t compound \t\t サイエンティスト\n",
      "12 サイエンティスト \t\t nmod \t\t 働い\n",
      "13 と \t\t case \t\t サイエンティスト\n",
      "14 し \t\t aux \t\t サイエンティスト\n",
      "15 て \t\t mark \t\t サイエンティスト\n",
      "16 一生懸命 \t\t amod \t\t 働い\n",
      "17 働い \t\t ROOT \t\t 働い\n",
      "18 て \t\t mark \t\t 働い\n",
      "19 い \t\t aux \t\t 働い\n",
      "20 ます \t\t aux \t\t 働い\n",
      "21 。 \t\t punct \t\t 働い\n",
      "22 場所 \t\t nsubj \t\t 新宿\n",
      "23 は \t\t case \t\t 場所\n",
      "24 東京都 \t\t nmod \t\t 新宿\n",
      "25 の \t\t case \t\t 東京都\n",
      "26 新宿 \t\t ROOT \t\t 新宿\n",
      "27 で \t\t case \t\t 新宿\n",
      "28 、 \t\t punct \t\t 新宿\n",
      "29 最高 \t\t nmod \t\t 街\n",
      "30 の \t\t case \t\t 最高\n",
      "31 街 \t\t punct \t\t 新宿\n",
      "32 です \t\t cop \t\t 街\n",
      "33 ! \t\t punct \t\t 街\n",
      "34 😊 \t\t dep \t\t 食べれ\n",
      "35 なん \t\t nmod \t\t と\n",
      "36 と \t\t advmod \t\t 食べれ\n",
      "37 ラーメン \t\t compound \t\t 一杯\n",
      "38 一杯 \t\t nsubj \t\t 食べれ\n",
      "39 が \t\t case \t\t 一杯\n",
      "40 ７００ \t\t nummod \t\t 円\n",
      "41 円 \t\t nmod \t\t 食べれ\n",
      "42 で \t\t case \t\t 円\n",
      "43 食べれ \t\t dep \t\t (^_^)\n",
      "44 ます \t\t aux \t\t 食べれ\n",
      "45 （ \t\t punct \t\t 食べれ\n",
      "46 令和 \t\t compound \t\t 食べれ\n",
      "47 元年 \t\t compound \t\t 食べれ\n",
      "48 １２ \t\t nummod \t\t 食べれ\n",
      "49 月 \t\t compound \t\t 食べれ\n",
      "50 １３ \t\t nummod \t\t 食べれ\n",
      "51 日 \t\t compound \t\t 食べれ\n",
      "52 現在 \t\t compound \t\t 食べれ\n",
      "53 ） \t\t punct \t\t 食べれ\n",
      "54 。 \t\t punct \t\t 食べれ\n",
      "55 (^_^) \t\t ROOT \t\t (^_^)\n"
     ]
    }
   ],
   "source": [
    "# トークンごとに番号とテキスト、依存関係をプリントする\n",
    "i = 0\n",
    "for トークン in 文章:\n",
    "    i += 1\n",
    "    print (f\"{i} {トークン.text} \\t\\t {トークン.dep_} \\t\\t {トークン.head.text}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">場所</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">東京都</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">の</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">新宿</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">最高</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1275\">の</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">街</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">NOUN</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"309.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1625\">です!</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">AUX</tspan>\n",
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       "\n",
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       "\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1450.0,266.5 L1458.0,254.5 1442.0,254.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4022a6f9dbd344778bab01b3d374c96f-2-8\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">cop</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1615.0,266.5 L1623.0,254.5 1607.0,254.5\" fill=\"currentColor\"/>\n",
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       "</svg>\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"ja\" id=\"4022a6f9dbd344778bab01b3d374c96f-3\" class=\"displacy\" width=\"3375\" height=\"924.5\" direction=\"ltr\" style=\"max-width: none; height: 924.5px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">😊</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">PUNCT</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">なん</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">PRON</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">と</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">ラーメン</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">一杯</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"925\">が</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">７００</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1275\">円</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">で</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1625\">食べれ</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1800\">AUX</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2500\">月</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">１３</tspan>\n",
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       "\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">NOUN</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M885.0,791.5 L893.0,779.5 877.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1120,791.5 L1112,779.5 1128,779.5\" fill=\"currentColor\"/>\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1295,791.5 L1287,779.5 1303,779.5\" fill=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1410.0,791.5 L1418.0,779.5 1402.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-9\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,2.0 3200.0,2.0 3200.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1645,791.5 L1637,779.5 1653,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1760.0,791.5 L1768.0,779.5 1752.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1940.0,791.5 L1948.0,779.5 1932.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-12\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,527.0 2120.0,527.0 2120.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2120.0,791.5 L2128.0,779.5 2112.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-13\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,439.5 2300.0,439.5 2300.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "        <textPath xlink:href=\"#arrow-4022a6f9dbd344778bab01b3d374c96f-3-13\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nummod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2300.0,791.5 L2308.0,779.5 2292.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-14\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,352.0 2480.0,352.0 2480.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4022a6f9dbd344778bab01b3d374c96f-3-14\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2480.0,791.5 L2488.0,779.5 2472.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M2660.0,791.5 L2668.0,779.5 2652.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-16\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,177.0 2840.0,177.0 2840.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M2840.0,791.5 L2848.0,779.5 2832.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4022a6f9dbd344778bab01b3d374c96f-3-17\" stroke-width=\"2px\" d=\"M1645,789.5 C1645,89.5 3020.0,89.5 3020.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4022a6f9dbd344778bab01b3d374c96f-3-17\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M3020.0,791.5 L3028.0,779.5 3012.0,779.5\" fill=\"currentColor\"/>\n",
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       "</svg>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 依存関係を視覚化する\n",
    "# 文に分割する\n",
    "文スパン = list(文章.sents)\n",
    "spacy.displacy.render(文スパン, style=\"dep\")"
   ]
  }
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